In order to properly maintain image forming devices such as printers, copiers, facsimile, and multi-function peripherals, remote management systems have been designed to collect various items of management information, such as maintenance management information, working state and failure information of the image forming devices. Most remote management systems are networked based. Thus, most image forming devices may be coupled to a communication network so that the connection between the image forming devices and the central management device is established via the network.
The central management device may collect information regarding the image forming device such as the number and kind of prints the image forming device has performed and other aspects of its current state and/or operation. This information may be stored so that historical records of incidents that occur for the image forming device may be maintained. An incident may include a hardware or software issues related to a specific part of the printing devices or a combination of the parts. For instance, a single degraded fuser may trigger often occurred paper jams. In another example, both a degraded output unit and degraded finisher unit may equally affect a rate of incidents because these units are mechanically connected to each other. In this example a threshold will be associated with a combination of an output unit and a finisher unit.
The image forming device needs to be available for normal operations, such as printing, scanning, copying and other functions for as long as possible during a designated time interval with a minimal number of errors. Failure ratios per day or/and per printed page need to be minimized. Some errors, such as paper jams may be caused by one or more degraded mechanical parts of the image forming device and may prevent the normal operation of the image forming device until the image forming device has been serviced.
Some image forming devices after being in service for a predetermined period of time may require maintenance work and/or parts replacement. In cases when the image forming device has a normal work load but produces more errors than it occurred before, the image forming device may need to be marked as a problematic device and maintenance and/or part replacement may need to be scheduled ahead of time. Such cases are different from scenarios where the image forming device's parts are broken, the customer cannot operate the device, and maintenance work may need to be provided on an emergency basis.
Diagnostics of problematic image forming devices may be developed based on information about the amount of errors monitored and the amount of pages printed. Presently, many existing systems use a value of Mean Time Between Failure (MTBF) as a factor to determine a health classification of the system. MTBF may be defined as an expected time between two failures for a repairable system. In general, many industrial devices are operated under similar conditions and the failure rate is pretty much stable and may be “characterized by a relatively constant failure rate”. In such eases, a linear regression function may be used to calculate a failure probability, and probability distribution matches criteria of normal distribution, when 99% of cases are covered in interval of 3 normal deviations.
Many industrial machines such as: computer hard drives, gas turbines, electrical motors, and the like may be operated under pretty well controlled environmental conditions, electrical or thermal conditions, rotation speed and other factors. Thus, calculating MTBF as a linear regression function may be used as a predictor for system/component failure. However, image forming devices may operate under very different customer conditions. For example, a work load for an image forming device could differentiate 100s of times, from 50 printed pages per day to 5,000 pages printed per day. In such cases, calculating a ‘mean’ value may not be accurately used as a classifier of ‘problematic’ devices.
Therefore, it would be desirable to provide a system and method that overcomes the above. The system and method would allow for differential diagnosis of defective components of an image forming device using a logistic regression model, wherein an image forming device may be presented with a set of incident counts associated with an electro-mechanical part, the incident counts identifying a level of degradation of the electro-mechanical part.